Overview

Dataset statistics

Number of variables46
Number of observations3376
Missing cells19952
Missing cells (%)12.8%
Total size in memory1.2 MiB
Average record size in memory361.0 B

Variable types

Numeric29
Text15
Boolean1
Unsupported1

Alerts

DataYear has constant value "2016"Constant
City has constant value "Seattle"Constant
State has constant value "WA"Constant
DefaultData is highly imbalanced (78.8%)Imbalance
SecondLargestPropertyUseType has 1697 (50.3%) missing valuesMissing
SecondLargestPropertyUseTypeGFA has 1697 (50.3%) missing valuesMissing
ThirdLargestPropertyUseType has 2780 (82.3%) missing valuesMissing
ThirdLargestPropertyUseTypeGFA has 2780 (82.3%) missing valuesMissing
YearsENERGYSTARCertified has 3257 (96.5%) missing valuesMissing
ENERGYSTARScore has 843 (25.0%) missing valuesMissing
Comments has 3376 (100.0%) missing valuesMissing
Outlier has 3344 (99.1%) missing valuesMissing
NumberofBuildings is highly skewed (γ1 = 43.39499472)Skewed
PropertyGFATotal is highly skewed (γ1 = 24.12940742)Skewed
PropertyGFABuilding(s) is highly skewed (γ1 = 27.62439064)Skewed
LargestPropertyUseTypeGFA is highly skewed (γ1 = 30.09595071)Skewed
SiteEnergyUse(kBtu) is highly skewed (γ1 = 24.84197927)Skewed
SteamUse(kBtu) is highly skewed (γ1 = 26.72088824)Skewed
Electricity(kWh) is highly skewed (γ1 = 28.72846386)Skewed
Electricity(kBtu) is highly skewed (γ1 = 28.72846389)Skewed
NaturalGas(therms) is highly skewed (γ1 = 30.03889031)Skewed
NaturalGas(kBtu) is highly skewed (γ1 = 30.03889028)Skewed
OSEBuildingID has unique valuesUnique
Comments is an unsupported type, check if it needs cleaning or further analysisUnsupported
NumberofBuildings has 92 (2.7%) zerosZeros
PropertyGFAParking has 2872 (85.1%) zerosZeros
SecondLargestPropertyUseTypeGFA has 126 (3.7%) zerosZeros
ThirdLargestPropertyUseTypeGFA has 48 (1.4%) zerosZeros
SourceEUIWN(kBtu/sf) has 36 (1.1%) zerosZeros
SteamUse(kBtu) has 3237 (95.9%) zerosZeros
NaturalGas(therms) has 1258 (37.3%) zerosZeros
NaturalGas(kBtu) has 1258 (37.3%) zerosZeros

Reproduction

Analysis started2024-06-19 20:00:34.495778
Analysis finished2024-06-19 20:00:35.068347
Duration0.57 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

OSEBuildingID
Real number (ℝ)

UNIQUE 

Distinct3376
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21208.99111
Minimum1
Maximum50226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:35.218529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile275.5
Q119990.75
median23112
Q325994.25
95-th percentile49784.25
Maximum50226
Range50225
Interquartile range (IQR)6003.5

Descriptive statistics

Standard deviation12223.75701
Coefficient of variation (CV)0.5763478776
Kurtosis0.6508667434
Mean21208.99111
Median Absolute Deviation (MAD)3012.5
Skewness-0.008278915001
Sum71601554
Variance149420235.6
MonotonicityNot monotonic
2024-06-19T22:00:35.417939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
25062 1
 
< 0.1%
24943 1
 
< 0.1%
24948 1
 
< 0.1%
24955 1
 
< 0.1%
24958 1
 
< 0.1%
24959 1
 
< 0.1%
24976 1
 
< 0.1%
24987 1
 
< 0.1%
24989 1
 
< 0.1%
Other values (3366) 3366
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
8 1
< 0.1%
ValueCountFrequency (%)
50226 1
< 0.1%
50225 1
< 0.1%
50224 1
< 0.1%
50223 1
< 0.1%
50222 1
< 0.1%

DataYear
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016
Minimum2016
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:35.577732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12016
median2016
Q32016
95-th percentile2016
Maximum2016
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean2016
Median Absolute Deviation (MAD)0
Skewness0
Sum6806016
Variance0
MonotonicityIncreasing
2024-06-19T22:00:35.720420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
2016 3376
100.0%
ValueCountFrequency (%)
2016 3376
100.0%
ValueCountFrequency (%)
2016 3376
100.0%
Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:35.876597image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length20
Median length20
Mean length17.16735782
Min length6

Characters and Unicode

Total characters57957
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNonResidential
2nd rowNonResidential
3rd rowNonResidential
4th rowNonResidential
5th rowNonResidential
ValueCountFrequency (%)
multifamily 1708
24.5%
nonresidential 1546
22.2%
lr 1018
14.6%
1-4 1018
14.6%
mr 580
 
8.3%
5-9 580
 
8.3%
hr 110
 
1.6%
10 110
 
1.6%
sps-district 98
 
1.4%
k-12 98
 
1.4%
Other values (3) 110
 
1.6%
2024-06-19T22:00:36.256680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6704
 
11.6%
l 4962
 
8.6%
3600
 
6.2%
t 3450
 
6.0%
a 3278
 
5.7%
R 3168
 
5.5%
n 3092
 
5.3%
e 3092
 
5.3%
M 2288
 
3.9%
- 1794
 
3.1%
Other values (30) 22529
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6704
 
11.6%
l 4962
 
8.6%
3600
 
6.2%
t 3450
 
6.0%
a 3278
 
5.7%
R 3168
 
5.5%
n 3092
 
5.3%
e 3092
 
5.3%
M 2288
 
3.9%
- 1794
 
3.1%
Other values (30) 22529
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6704
 
11.6%
l 4962
 
8.6%
3600
 
6.2%
t 3450
 
6.0%
a 3278
 
5.7%
R 3168
 
5.5%
n 3092
 
5.3%
e 3092
 
5.3%
M 2288
 
3.9%
- 1794
 
3.1%
Other values (30) 22529
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6704
 
11.6%
l 4962
 
8.6%
3600
 
6.2%
t 3450
 
6.0%
a 3278
 
5.7%
R 3168
 
5.5%
n 3092
 
5.3%
e 3092
 
5.3%
M 2288
 
3.9%
- 1794
 
3.1%
Other values (30) 22529
38.9%
Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:36.468050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length27
Median length22
Mean length17.18927725
Min length5

Characters and Unicode

Total characters58031
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel
ValueCountFrequency (%)
multifamily 1656
23.6%
low-rise 987
14.1%
mid-rise 564
 
8.0%
office 508
 
7.2%
small 293
 
4.2%
and 293
 
4.2%
mid-sized 293
 
4.2%
other 256
 
3.6%
warehouse 199
 
2.8%
large 173
 
2.5%
Other values (28) 1794
25.6%
2024-06-19T22:00:36.856186image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 7607
 
13.1%
e 4535
 
7.8%
l 4427
 
7.6%
3640
 
6.3%
a 3045
 
5.2%
t 2796
 
4.8%
f 2712
 
4.7%
M 2685
 
4.6%
- 2409
 
4.2%
s 2182
 
3.8%
Other values (33) 21993
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58031
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7607
 
13.1%
e 4535
 
7.8%
l 4427
 
7.6%
3640
 
6.3%
a 3045
 
5.2%
t 2796
 
4.8%
f 2712
 
4.7%
M 2685
 
4.6%
- 2409
 
4.2%
s 2182
 
3.8%
Other values (33) 21993
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58031
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7607
 
13.1%
e 4535
 
7.8%
l 4427
 
7.6%
3640
 
6.3%
a 3045
 
5.2%
t 2796
 
4.8%
f 2712
 
4.7%
M 2685
 
4.6%
- 2409
 
4.2%
s 2182
 
3.8%
Other values (33) 21993
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58031
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7607
 
13.1%
e 4535
 
7.8%
l 4427
 
7.6%
3640
 
6.3%
a 3045
 
5.2%
t 2796
 
4.8%
f 2712
 
4.7%
M 2685
 
4.6%
- 2409
 
4.2%
s 2182
 
3.8%
Other values (33) 21993
37.9%
Distinct3362
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:37.247254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length72
Median length53
Mean length19.40017773
Min length2

Characters and Unicode

Total characters65495
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3349 ?
Unique (%)99.2%

Sample

1st rowMayflower park hotel
2nd rowParamount Hotel
3rd row5673-The Westin Seattle
4th rowHOTEL MAX
5th rowWARWICK SEATTLE HOTEL (ID8)
ValueCountFrequency (%)
apartments 426
 
4.3%
building 329
 
3.4%
248
 
2.5%
the 166
 
1.7%
seattle 158
 
1.6%
center 127
 
1.3%
condominium 123
 
1.3%
court 104
 
1.1%
park 103
 
1.1%
place 101
 
1.0%
Other values (3149) 7914
80.8%
2024-06-19T22:00:37.882613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6431
 
9.8%
e 5232
 
8.0%
a 3917
 
6.0%
t 3857
 
5.9%
n 3820
 
5.8%
i 3400
 
5.2%
r 3374
 
5.2%
o 3084
 
4.7%
l 2584
 
3.9%
s 2148
 
3.3%
Other values (66) 27648
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6431
 
9.8%
e 5232
 
8.0%
a 3917
 
6.0%
t 3857
 
5.9%
n 3820
 
5.8%
i 3400
 
5.2%
r 3374
 
5.2%
o 3084
 
4.7%
l 2584
 
3.9%
s 2148
 
3.3%
Other values (66) 27648
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6431
 
9.8%
e 5232
 
8.0%
a 3917
 
6.0%
t 3857
 
5.9%
n 3820
 
5.8%
i 3400
 
5.2%
r 3374
 
5.2%
o 3084
 
4.7%
l 2584
 
3.9%
s 2148
 
3.3%
Other values (66) 27648
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6431
 
9.8%
e 5232
 
8.0%
a 3917
 
6.0%
t 3857
 
5.9%
n 3820
 
5.8%
i 3400
 
5.2%
r 3374
 
5.2%
o 3084
 
4.7%
l 2584
 
3.9%
s 2148
 
3.3%
Other values (66) 27648
42.2%
Distinct3354
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:38.167409image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length41
Median length34
Mean length17.23992891
Min length8

Characters and Unicode

Total characters58202
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3338 ?
Unique (%)98.9%

Sample

1st row405 Olive way
2nd row724 Pine street
3rd row1900 5th Avenue
4th row620 STEWART ST
5th row401 LENORA ST
ValueCountFrequency (%)
ave 1608
 
12.5%
st 590
 
4.6%
ne 462
 
3.6%
s 462
 
3.6%
n 407
 
3.2%
avenue 388
 
3.0%
way 321
 
2.5%
e 309
 
2.4%
sw 230
 
1.8%
street 225
 
1.7%
Other values (2292) 7872
61.1%
2024-06-19T22:00:38.716249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9521
 
16.4%
e 4223
 
7.3%
1 3286
 
5.6%
t 2929
 
5.0%
0 2832
 
4.9%
A 2299
 
4.0%
v 1977
 
3.4%
2 1969
 
3.4%
S 1874
 
3.2%
n 1638
 
2.8%
Other values (60) 25654
44.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9521
 
16.4%
e 4223
 
7.3%
1 3286
 
5.6%
t 2929
 
5.0%
0 2832
 
4.9%
A 2299
 
4.0%
v 1977
 
3.4%
2 1969
 
3.4%
S 1874
 
3.2%
n 1638
 
2.8%
Other values (60) 25654
44.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9521
 
16.4%
e 4223
 
7.3%
1 3286
 
5.6%
t 2929
 
5.0%
0 2832
 
4.9%
A 2299
 
4.0%
v 1977
 
3.4%
2 1969
 
3.4%
S 1874
 
3.2%
n 1638
 
2.8%
Other values (60) 25654
44.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9521
 
16.4%
e 4223
 
7.3%
1 3286
 
5.6%
t 2929
 
5.0%
0 2832
 
4.9%
A 2299
 
4.0%
v 1977
 
3.4%
2 1969
 
3.4%
S 1874
 
3.2%
n 1638
 
2.8%
Other values (60) 25654
44.1%

City
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:38.873719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters23632
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeattle
2nd rowSeattle
3rd rowSeattle
4th rowSeattle
5th rowSeattle
ValueCountFrequency (%)
seattle 3376
100.0%
2024-06-19T22:00:39.172734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6752
28.6%
t 6752
28.6%
S 3376
14.3%
a 3376
14.3%
l 3376
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6752
28.6%
t 6752
28.6%
S 3376
14.3%
a 3376
14.3%
l 3376
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6752
28.6%
t 6752
28.6%
S 3376
14.3%
a 3376
14.3%
l 3376
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6752
28.6%
t 6752
28.6%
S 3376
14.3%
a 3376
14.3%
l 3376
14.3%

State
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:39.272560image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6752
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA
ValueCountFrequency (%)
wa 3376
100.0%
2024-06-19T22:00:39.552966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 3376
50.0%
A 3376
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 3376
50.0%
A 3376
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 3376
50.0%
A 3376
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 3376
50.0%
A 3376
50.0%

ZipCode
Real number (ℝ)

Distinct55
Distinct (%)1.6%
Missing16
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean98116.94911
Minimum98006
Maximum98272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:39.739841image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum98006
5-th percentile98101
Q198105
median98115
Q398122
95-th percentile98144
Maximum98272
Range266
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.61520454
Coefficient of variation (CV)0.0001897246573
Kurtosis10.49296463
Mean98116.94911
Median Absolute Deviation (MAD)10
Skewness1.99966218
Sum329672949
Variance346.5258402
MonotonicityNot monotonic
2024-06-19T22:00:39.949638image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98109 294
 
8.7%
98104 251
 
7.4%
98122 243
 
7.2%
98101 230
 
6.8%
98105 191
 
5.7%
98134 186
 
5.5%
98121 186
 
5.5%
98102 169
 
5.0%
98119 167
 
4.9%
98103 161
 
4.8%
Other values (45) 1282
38.0%
ValueCountFrequency (%)
98006 1
< 0.1%
98011 1
< 0.1%
98012 1
< 0.1%
98013 2
0.1%
98020 1
< 0.1%
ValueCountFrequency (%)
98272 1
 
< 0.1%
98204 1
 
< 0.1%
98199 70
2.1%
98198 1
 
< 0.1%
98195 10
 
0.3%
Distinct3268
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:40.217446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length25
Median length10
Mean length10.00503555
Min length9

Characters and Unicode

Total characters33777
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3191 ?
Unique (%)94.5%

Sample

1st row0659000030
2nd row0659000220
3rd row0659000475
4th row0659000640
5th row0659000970
ValueCountFrequency (%)
1625049001 8
 
0.2%
0925049346 5
 
0.1%
0002400002 5
 
0.1%
3224049012 5
 
0.1%
3624039009 4
 
0.1%
8632880000 4
 
0.1%
7666203240 4
 
0.1%
0225049077 3
 
0.1%
8809700040 3
 
0.1%
1985200003 3
 
0.1%
Other values (3259) 3334
98.7%
2024-06-19T22:00:40.703240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11368
33.7%
2 3168
 
9.4%
5 2946
 
8.7%
6 2713
 
8.0%
1 2691
 
8.0%
9 2377
 
7.0%
7 2363
 
7.0%
4 2164
 
6.4%
3 2071
 
6.1%
8 1909
 
5.7%
Other values (5) 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33777
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11368
33.7%
2 3168
 
9.4%
5 2946
 
8.7%
6 2713
 
8.0%
1 2691
 
8.0%
9 2377
 
7.0%
7 2363
 
7.0%
4 2164
 
6.4%
3 2071
 
6.1%
8 1909
 
5.7%
Other values (5) 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33777
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11368
33.7%
2 3168
 
9.4%
5 2946
 
8.7%
6 2713
 
8.0%
1 2691
 
8.0%
9 2377
 
7.0%
7 2363
 
7.0%
4 2164
 
6.4%
3 2071
 
6.1%
8 1909
 
5.7%
Other values (5) 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33777
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11368
33.7%
2 3168
 
9.4%
5 2946
 
8.7%
6 2713
 
8.0%
1 2691
 
8.0%
9 2377
 
7.0%
7 2363
 
7.0%
4 2164
 
6.4%
3 2071
 
6.1%
8 1909
 
5.7%
Other values (5) 7
 
< 0.1%

CouncilDistrictCode
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.439277251
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:40.877914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.120625473
Coefficient of variation (CV)0.4776961097
Kurtosis-1.444891233
Mean4.439277251
Median Absolute Deviation (MAD)2
Skewness-0.07015381123
Sum14987
Variance4.497052396
MonotonicityNot monotonic
2024-06-19T22:00:41.032767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 1037
30.7%
3 596
17.7%
2 509
15.1%
4 367
 
10.9%
5 338
 
10.0%
1 282
 
8.4%
6 247
 
7.3%
ValueCountFrequency (%)
1 282
8.4%
2 509
15.1%
3 596
17.7%
4 367
10.9%
5 338
10.0%
ValueCountFrequency (%)
7 1037
30.7%
6 247
 
7.3%
5 338
 
10.0%
4 367
 
10.9%
3 596
17.7%
Distinct19
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:41.214668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length16
Mean length10.11404028
Min length4

Characters and Unicode

Total characters34145
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDOWNTOWN
2nd rowDOWNTOWN
3rd rowDOWNTOWN
4th rowDOWNTOWN
5th rowDOWNTOWN
ValueCountFrequency (%)
downtown 573
10.9%
east 453
 
8.6%
magnolia 423
 
8.0%
423
 
8.0%
queen 423
 
8.0%
anne 423
 
8.0%
greater 375
 
7.1%
duwamish 375
 
7.1%
northeast 280
 
5.3%
union 251
 
4.8%
Other values (9) 1273
24.1%
2024-06-19T22:00:41.627314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 4136
12.1%
E 3744
11.0%
A 3457
10.1%
T 3155
 
9.2%
O 2719
 
8.0%
W 1897
 
5.6%
1896
 
5.6%
S 1841
 
5.4%
R 1700
 
5.0%
U 1310
 
3.8%
Other values (24) 8290
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 4136
12.1%
E 3744
11.0%
A 3457
10.1%
T 3155
 
9.2%
O 2719
 
8.0%
W 1897
 
5.6%
1896
 
5.6%
S 1841
 
5.4%
R 1700
 
5.0%
U 1310
 
3.8%
Other values (24) 8290
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 4136
12.1%
E 3744
11.0%
A 3457
10.1%
T 3155
 
9.2%
O 2719
 
8.0%
W 1897
 
5.6%
1896
 
5.6%
S 1841
 
5.4%
R 1700
 
5.0%
U 1310
 
3.8%
Other values (24) 8290
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 4136
12.1%
E 3744
11.0%
A 3457
10.1%
T 3155
 
9.2%
O 2719
 
8.0%
W 1897
 
5.6%
1896
 
5.6%
S 1841
 
5.4%
R 1700
 
5.0%
U 1310
 
3.8%
Other values (24) 8290
24.3%

Latitude
Real number (ℝ)

Distinct2876
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.62403312
Minimum47.49917
Maximum47.73387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:41.827178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum47.49917
5-th percentile47.5417125
Q147.59986
median47.618675
Q347.657115
95-th percentile47.713
Maximum47.73387
Range0.2347
Interquartile range (IQR)0.057255

Descriptive statistics

Standard deviation0.04775842495
Coefficient of variation (CV)0.001002821933
Kurtosis-0.1411603852
Mean47.62403312
Median Absolute Deviation (MAD)0.028385
Skewness0.1400447677
Sum160778.7358
Variance0.002280867154
MonotonicityNot monotonic
2024-06-19T22:00:42.032660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.66246 9
 
0.3%
47.61598 7
 
0.2%
47.62208 6
 
0.2%
47.62395 5
 
0.1%
47.61543 5
 
0.1%
47.52549 5
 
0.1%
47.6139 4
 
0.1%
47.61048 4
 
0.1%
47.60071 4
 
0.1%
47.59938 4
 
0.1%
Other values (2866) 3323
98.4%
ValueCountFrequency (%)
47.49917 1
< 0.1%
47.50061895 1
< 0.1%
47.50224 1
< 0.1%
47.50959 1
< 0.1%
47.5097 1
< 0.1%
ValueCountFrequency (%)
47.73387 1
< 0.1%
47.73375 1
< 0.1%
47.73368 1
< 0.1%
47.7336 1
< 0.1%
47.73357 1
< 0.1%

Longitude
Real number (ℝ)

Distinct2656
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.3347952
Minimum-122.41425
Maximum-122.2209659
Zeros0
Zeros (%)0.0%
Negative3376
Negative (%)100.0%
Memory size26.5 KiB
2024-06-19T22:00:42.225526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-122.41425
5-th percentile-122.3865425
Q1-122.3506625
median-122.332495
Q3-122.3194075
95-th percentile-122.2898275
Maximum-122.2209659
Range0.1932841
Interquartile range (IQR)0.031255

Descriptive statistics

Standard deviation0.02720328528
Coefficient of variation (CV)-0.0002223675221
Kurtosis0.2623984964
Mean-122.3347952
Median Absolute Deviation (MAD)0.01513
Skewness-0.1375264709
Sum-413002.2686
Variance0.00074001873
MonotonicityNot monotonic
2024-06-19T22:00:42.430236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29898 8
 
0.2%
-122.35398 7
 
0.2%
-122.32468 6
 
0.2%
-122.33369 6
 
0.2%
-122.33379 6
 
0.2%
-122.33064 5
 
0.1%
-122.31769 5
 
0.1%
-122.32417 5
 
0.1%
-122.32592 5
 
0.1%
-122.32585 4
 
0.1%
Other values (2646) 3319
98.3%
ValueCountFrequency (%)
-122.41425 1
< 0.1%
-122.41182 1
< 0.1%
-122.41178 1
< 0.1%
-122.41169 1
< 0.1%
-122.41037 1
< 0.1%
ValueCountFrequency (%)
-122.2209659 1
< 0.1%
-122.25864 1
< 0.1%
-122.26028 1
< 0.1%
-122.26034 1
< 0.1%
-122.26166 2
0.1%

YearBuilt
Real number (ℝ)

Distinct113
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.573164
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:42.642067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1908
Q11948
median1975
Q31997
95-th percentile2012
Maximum2015
Range115
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.08815578
Coefficient of variation (CV)0.01680819204
Kurtosis-0.8713417711
Mean1968.573164
Median Absolute Deviation (MAD)24
Skewness-0.5394445573
Sum6645903
Variance1094.826053
MonotonicityNot monotonic
2024-06-19T22:00:42.875038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 72
 
2.1%
2014 67
 
2.0%
1989 67
 
2.0%
2008 66
 
2.0%
1988 64
 
1.9%
1999 64
 
1.9%
1968 63
 
1.9%
1990 60
 
1.8%
2001 60
 
1.8%
2002 59
 
1.7%
Other values (103) 2734
81.0%
ValueCountFrequency (%)
1900 55
1.6%
1901 8
 
0.2%
1902 11
 
0.3%
1903 4
 
0.1%
1904 15
 
0.4%
ValueCountFrequency (%)
2015 37
1.1%
2014 67
2.0%
2013 51
1.5%
2012 35
1.0%
2011 15
 
0.4%

NumberofBuildings
Real number (ℝ)

SKEWED  ZEROS 

Distinct17
Distinct (%)0.5%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.106888361
Minimum0
Maximum111
Zeros92
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:43.048712image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum111
Range111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.108401751
Coefficient of variation (CV)1.904800723
Kurtosis2205.296217
Mean1.106888361
Median Absolute Deviation (MAD)0
Skewness43.39499472
Sum3728
Variance4.445357942
MonotonicityNot monotonic
2024-06-19T22:00:43.208716image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3175
94.0%
0 92
 
2.7%
2 37
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
5 10
 
0.3%
6 5
 
0.1%
8 3
 
0.1%
10 2
 
0.1%
9 2
 
0.1%
Other values (7) 8
 
0.2%
(Missing) 8
 
0.2%
ValueCountFrequency (%)
0 92
 
2.7%
1 3175
94.0%
2 37
 
1.1%
3 22
 
0.7%
4 12
 
0.4%
ValueCountFrequency (%)
111 1
< 0.1%
27 1
< 0.1%
23 1
< 0.1%
16 1
< 0.1%
14 2
0.1%

NumberofFloors
Real number (ℝ)

Distinct50
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.709123223
Minimum0
Maximum99
Zeros16
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:43.398997image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile12
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.494464797
Coefficient of variation (CV)1.166770232
Kurtosis55.95064463
Mean4.709123223
Median Absolute Deviation (MAD)2
Skewness5.922339745
Sum15898
Variance30.18914341
MonotonicityNot monotonic
2024-06-19T22:00:43.892810image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 692
20.5%
3 692
20.5%
1 466
13.8%
2 439
13.0%
6 306
9.1%
5 295
8.7%
7 148
 
4.4%
8 64
 
1.9%
10 32
 
0.9%
11 32
 
0.9%
Other values (40) 210
 
6.2%
ValueCountFrequency (%)
0 16
 
0.5%
1 466
13.8%
2 439
13.0%
3 692
20.5%
4 692
20.5%
ValueCountFrequency (%)
99 1
< 0.1%
76 1
< 0.1%
63 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%

PropertyGFATotal
Real number (ℝ)

SKEWED 

Distinct3195
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94833.53732
Minimum11285
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:44.095093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum11285
5-th percentile21291.5
Q128487
median44175
Q390992
95-th percentile320096
Maximum9320156
Range9308871
Interquartile range (IQR)62505

Descriptive statistics

Standard deviation218837.6071
Coefficient of variation (CV)2.30759722
Kurtosis946.2394908
Mean94833.53732
Median Absolute Deviation (MAD)19739.5
Skewness24.12940742
Sum320158022
Variance4.788989829 × 1010
MonotonicityNot monotonic
2024-06-19T22:00:44.337111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
28800 7
 
0.2%
21600 7
 
0.2%
24000 6
 
0.2%
22320 4
 
0.1%
30720 4
 
0.1%
30240 4
 
0.1%
43380 3
 
0.1%
31900 3
 
0.1%
Other values (3185) 3321
98.4%
ValueCountFrequency (%)
11285 1
< 0.1%
11685 1
< 0.1%
11968 1
< 0.1%
12294 1
< 0.1%
12769 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1952220 1
< 0.1%
1765970 1
< 0.1%
1605578 1
< 0.1%

PropertyGFAParking
Real number (ℝ)

ZEROS 

Distinct496
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8001.526066
Minimum0
Maximum512608
Zeros2872
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:44.534580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile46400.75
Maximum512608
Range512608
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32326.72393
Coefficient of variation (CV)4.040069814
Kurtosis58.97489179
Mean8001.526066
Median Absolute Deviation (MAD)0
Skewness6.651190825
Sum27013152
Variance1045017080
MonotonicityNot monotonic
2024-06-19T22:00:44.789702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2872
85.1%
13320 3
 
0.1%
10800 2
 
0.1%
20416 2
 
0.1%
30000 2
 
0.1%
22000 2
 
0.1%
100176 2
 
0.1%
25800 2
 
0.1%
12960 2
 
0.1%
756 1
 
< 0.1%
Other values (486) 486
 
14.4%
ValueCountFrequency (%)
0 2872
85.1%
38 1
 
< 0.1%
260 1
 
< 0.1%
415 1
 
< 0.1%
604 1
 
< 0.1%
ValueCountFrequency (%)
512608 1
< 0.1%
407795 1
< 0.1%
389860 1
< 0.1%
368980 1
< 0.1%
335109 1
< 0.1%

PropertyGFABuilding(s)
Real number (ℝ)

SKEWED 

Distinct3193
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86832.01126
Minimum3636
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:45.046830image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21021
Q127756
median43216
Q384276.25
95-th percentile282658.5
Maximum9320156
Range9316520
Interquartile range (IQR)56520.25

Descriptive statistics

Standard deviation207939.8119
Coefficient of variation (CV)2.39473679
Kurtosis1161.360271
Mean86832.01126
Median Absolute Deviation (MAD)18958.5
Skewness27.62439064
Sum293144870
Variance4.323896538 × 1010
MonotonicityNot monotonic
2024-06-19T22:00:45.253876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36000 9
 
0.3%
25920 8
 
0.2%
21600 7
 
0.2%
28800 7
 
0.2%
24000 6
 
0.2%
30240 4
 
0.1%
22320 4
 
0.1%
30720 4
 
0.1%
25800 3
 
0.1%
31900 3
 
0.1%
Other values (3183) 3321
98.4%
ValueCountFrequency (%)
3636 1
< 0.1%
10925 1
< 0.1%
11285 1
< 0.1%
11440 1
< 0.1%
11685 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
2200000 1
< 0.1%
1765970 1
< 0.1%
1632820 1
< 0.1%
1592914 1
< 0.1%
Distinct466
Distinct (%)13.8%
Missing9
Missing (%)0.3%
Memory size26.5 KiB
2024-06-19T22:00:45.515358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length255
Median length162
Mean length25.93436293
Min length5

Characters and Unicode

Total characters87321
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314 ?
Unique (%)9.3%

Sample

1st rowHotel
2nd rowHotel, Parking, Restaurant
3rd rowHotel
4th rowHotel
5th rowHotel, Parking, Swimming Pool
ValueCountFrequency (%)
multifamily 1707
17.2%
housing 1707
17.2%
parking 1087
11.0%
office 958
 
9.7%
store 472
 
4.8%
other 419
 
4.2%
retail 404
 
4.1%
warehouse 278
 
2.8%
non-refrigerated 261
 
2.6%
181
 
1.8%
Other values (97) 2430
24.5%
2024-06-19T22:00:46.042445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 9364
 
10.7%
6537
 
7.5%
e 5683
 
6.5%
a 5252
 
6.0%
l 4957
 
5.7%
t 4946
 
5.7%
u 4261
 
4.9%
r 4247
 
4.9%
n 4177
 
4.8%
o 4014
 
4.6%
Other values (42) 33883
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 9364
 
10.7%
6537
 
7.5%
e 5683
 
6.5%
a 5252
 
6.0%
l 4957
 
5.7%
t 4946
 
5.7%
u 4261
 
4.9%
r 4247
 
4.9%
n 4177
 
4.8%
o 4014
 
4.6%
Other values (42) 33883
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 9364
 
10.7%
6537
 
7.5%
e 5683
 
6.5%
a 5252
 
6.0%
l 4957
 
5.7%
t 4946
 
5.7%
u 4261
 
4.9%
r 4247
 
4.9%
n 4177
 
4.8%
o 4014
 
4.6%
Other values (42) 33883
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 9364
 
10.7%
6537
 
7.5%
e 5683
 
6.5%
a 5252
 
6.0%
l 4957
 
5.7%
t 4946
 
5.7%
u 4261
 
4.9%
r 4247
 
4.9%
n 4177
 
4.8%
o 4014
 
4.6%
Other values (42) 33883
38.8%
Distinct56
Distinct (%)1.7%
Missing20
Missing (%)0.6%
Memory size26.5 KiB
2024-06-19T22:00:46.320210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length52
Median length19
Mean length16.25595948
Min length5

Characters and Unicode

Total characters54555
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.3%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel
ValueCountFrequency (%)
multifamily 1667
27.1%
housing 1667
27.1%
office 543
 
8.8%
warehouse 211
 
3.4%
non-refrigerated 199
 
3.2%
other 179
 
2.9%
store 140
 
2.3%
k-12 139
 
2.3%
school 139
 
2.3%
facility 100
 
1.6%
Other values (79) 1168
19.0%
2024-06-19T22:00:46.781615image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6825
 
12.5%
l 4130
 
7.6%
u 3794
 
7.0%
t 3095
 
5.7%
o 3071
 
5.6%
e 3031
 
5.6%
f 3005
 
5.5%
a 2800
 
5.1%
2796
 
5.1%
n 2391
 
4.4%
Other values (41) 19617
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6825
 
12.5%
l 4130
 
7.6%
u 3794
 
7.0%
t 3095
 
5.7%
o 3071
 
5.6%
e 3031
 
5.6%
f 3005
 
5.5%
a 2800
 
5.1%
2796
 
5.1%
n 2391
 
4.4%
Other values (41) 19617
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6825
 
12.5%
l 4130
 
7.6%
u 3794
 
7.0%
t 3095
 
5.7%
o 3071
 
5.6%
e 3031
 
5.6%
f 3005
 
5.5%
a 2800
 
5.1%
2796
 
5.1%
n 2391
 
4.4%
Other values (41) 19617
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6825
 
12.5%
l 4130
 
7.6%
u 3794
 
7.0%
t 3095
 
5.7%
o 3071
 
5.6%
e 3031
 
5.6%
f 3005
 
5.5%
a 2800
 
5.1%
2796
 
5.1%
n 2391
 
4.4%
Other values (41) 19617
36.0%

LargestPropertyUseTypeGFA
Real number (ℝ)

SKEWED 

Distinct3122
Distinct (%)93.0%
Missing20
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean79177.63856
Minimum5656
Maximum9320156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:47.006626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5656
5-th percentile17609
Q125094.75
median39894
Q376200.25
95-th percentile243388.5
Maximum9320156
Range9314500
Interquartile range (IQR)51105.5

Descriptive statistics

Standard deviation201703.4075
Coefficient of variation (CV)2.547479455
Kurtosis1320.609838
Mean79177.63856
Median Absolute Deviation (MAD)17574
Skewness30.09595071
Sum265720155
Variance4.068426459 × 1010
MonotonicityNot monotonic
2024-06-19T22:00:47.252346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22000 9
 
0.3%
24000 9
 
0.3%
30000 8
 
0.2%
21600 8
 
0.2%
20000 7
 
0.2%
25000 6
 
0.2%
28800 5
 
0.1%
45000 5
 
0.1%
36000 5
 
0.1%
15000 5
 
0.1%
Other values (3112) 3289
97.4%
(Missing) 20
 
0.6%
ValueCountFrequency (%)
5656 1
< 0.1%
6455 1
< 0.1%
6601 1
< 0.1%
6900 1
< 0.1%
7245 1
< 0.1%
ValueCountFrequency (%)
9320156 1
< 0.1%
1719643 1
< 0.1%
1680937 1
< 0.1%
1639334 1
< 0.1%
1585960 1
< 0.1%
Distinct50
Distinct (%)3.0%
Missing1697
Missing (%)50.3%
Memory size26.5 KiB
2024-06-19T22:00:47.505298image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length52
Median length7
Mean length9.177486599
Min length5

Characters and Unicode

Total characters15409
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.7%

Sample

1st rowParking
2nd rowParking
3rd rowParking
4th rowParking
5th rowParking
ValueCountFrequency (%)
parking 976
45.5%
office 228
 
10.6%
store 170
 
7.9%
retail 155
 
7.2%
other 94
 
4.4%
restaurant 40
 
1.9%
37
 
1.7%
warehouse 35
 
1.6%
non-refrigerated 33
 
1.5%
services 20
 
0.9%
Other values (74) 355
 
16.6%
2024-06-19T22:00:47.932142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 1647
10.7%
r 1600
10.4%
a 1494
 
9.7%
n 1222
 
7.9%
e 1193
 
7.7%
g 1059
 
6.9%
P 1002
 
6.5%
k 1001
 
6.5%
t 773
 
5.0%
f 517
 
3.4%
Other values (42) 3901
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1647
10.7%
r 1600
10.4%
a 1494
 
9.7%
n 1222
 
7.9%
e 1193
 
7.7%
g 1059
 
6.9%
P 1002
 
6.5%
k 1001
 
6.5%
t 773
 
5.0%
f 517
 
3.4%
Other values (42) 3901
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1647
10.7%
r 1600
10.4%
a 1494
 
9.7%
n 1222
 
7.9%
e 1193
 
7.7%
g 1059
 
6.9%
P 1002
 
6.5%
k 1001
 
6.5%
t 773
 
5.0%
f 517
 
3.4%
Other values (42) 3901
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1647
10.7%
r 1600
10.4%
a 1494
 
9.7%
n 1222
 
7.9%
e 1193
 
7.7%
g 1059
 
6.9%
P 1002
 
6.5%
k 1001
 
6.5%
t 773
 
5.0%
f 517
 
3.4%
Other values (42) 3901
25.3%

SecondLargestPropertyUseTypeGFA
Real number (ℝ)

MISSING  ZEROS 

Distinct1352
Distinct (%)80.5%
Missing1697
Missing (%)50.3%
Infinite0
Infinite (%)0.0%
Mean28444.07582
Minimum0
Maximum686750
Zeros126
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:48.169703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15000
median10664
Q326640
95-th percentile117338.6
Maximum686750
Range686750
Interquartile range (IQR)21640

Descriptive statistics

Standard deviation54392.91793
Coefficient of variation (CV)1.912275803
Kurtosis36.30208308
Mean28444.07582
Median Absolute Deviation (MAD)7564
Skewness5.033480723
Sum47757603.3
Variance2958589521
MonotonicityNot monotonic
2024-06-19T22:00:48.410500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126
 
3.7%
5000 14
 
0.4%
7200 12
 
0.4%
15000 12
 
0.4%
6000 12
 
0.4%
7000 9
 
0.3%
10000 8
 
0.2%
8000 7
 
0.2%
4000 7
 
0.2%
1500 6
 
0.2%
Other values (1342) 1466
43.4%
(Missing) 1697
50.3%
ValueCountFrequency (%)
0 126
3.7%
2 1
 
< 0.1%
40 1
 
< 0.1%
200 1
 
< 0.1%
220 1
 
< 0.1%
ValueCountFrequency (%)
686750 1
< 0.1%
639931 1
< 0.1%
441551 1
< 0.1%
438756 1
< 0.1%
389860 1
< 0.1%
Distinct44
Distinct (%)7.4%
Missing2780
Missing (%)82.3%
Memory size26.5 KiB
2024-06-19T22:00:48.693817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length52
Median length27
Mean length11.9966443
Min length5

Characters and Unicode

Total characters7150
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)1.5%

Sample

1st rowRestaurant
2nd rowSwimming Pool
3rd rowData Center
4th rowSwimming Pool
5th rowOffice
ValueCountFrequency (%)
office 127
13.1%
store 115
 
11.9%
retail 110
 
11.3%
other 77
 
7.9%
parking 71
 
7.3%
restaurant 59
 
6.1%
swimming 29
 
3.0%
pool 29
 
3.0%
28
 
2.9%
warehouse 20
 
2.1%
Other values (59) 305
31.4%
2024-06-19T22:00:49.148899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 845
 
11.8%
t 617
 
8.6%
i 569
 
8.0%
a 562
 
7.9%
r 518
 
7.2%
374
 
5.2%
o 333
 
4.7%
n 328
 
4.6%
l 303
 
4.2%
f 289
 
4.0%
Other values (41) 2412
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 845
 
11.8%
t 617
 
8.6%
i 569
 
8.0%
a 562
 
7.9%
r 518
 
7.2%
374
 
5.2%
o 333
 
4.7%
n 328
 
4.6%
l 303
 
4.2%
f 289
 
4.0%
Other values (41) 2412
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 845
 
11.8%
t 617
 
8.6%
i 569
 
8.0%
a 562
 
7.9%
r 518
 
7.2%
374
 
5.2%
o 333
 
4.7%
n 328
 
4.6%
l 303
 
4.2%
f 289
 
4.0%
Other values (41) 2412
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 845
 
11.8%
t 617
 
8.6%
i 569
 
8.0%
a 562
 
7.9%
r 518
 
7.2%
374
 
5.2%
o 333
 
4.7%
n 328
 
4.6%
l 303
 
4.2%
f 289
 
4.0%
Other values (41) 2412
33.7%

ThirdLargestPropertyUseTypeGFA
Real number (ℝ)

MISSING  ZEROS 

Distinct501
Distinct (%)84.1%
Missing2780
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean11738.67517
Minimum0
Maximum459748
Zeros48
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:49.388628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12239
median5043
Q310138.75
95-th percentile41654.5
Maximum459748
Range459748
Interquartile range (IQR)7899.75

Descriptive statistics

Standard deviation29331.19929
Coefficient of variation (CV)2.498680547
Kurtosis114.1871141
Mean11738.67517
Median Absolute Deviation (MAD)3590.5
Skewness9.196935797
Sum6996250.399
Variance860319251.6
MonotonicityNot monotonic
2024-06-19T22:00:49.591244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
 
1.4%
6000 7
 
0.2%
5000 6
 
0.2%
2000 5
 
0.1%
3000 5
 
0.1%
1000 4
 
0.1%
9000 4
 
0.1%
6200 3
 
0.1%
1250 3
 
0.1%
1500 3
 
0.1%
Other values (491) 508
 
15.0%
(Missing) 2780
82.3%
ValueCountFrequency (%)
0 48
1.4%
182 1
 
< 0.1%
187 1
 
< 0.1%
240 1
 
< 0.1%
250 1
 
< 0.1%
ValueCountFrequency (%)
459748 1
< 0.1%
303910 1
< 0.1%
220303 1
< 0.1%
177210 1
< 0.1%
141450 1
< 0.1%
Distinct65
Distinct (%)54.6%
Missing3257
Missing (%)96.5%
Memory size26.5 KiB
2024-06-19T22:00:49.784242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length60
Median length52
Mean length12.23529412
Min length4

Characters and Unicode

Total characters1456
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)41.2%

Sample

1st row2016
2nd row2016
3rd row2014
4th row2016
5th row2016
ValueCountFrequency (%)
2016 14
 
11.8%
20172016 8
 
6.7%
2017 7
 
5.9%
2014 6
 
5.0%
20162015 6
 
5.0%
2013 4
 
3.4%
2009 4
 
3.4%
20172015 3
 
2.5%
201620152014 3
 
2.5%
20152014 3
 
2.5%
Other values (55) 61
51.3%
2024-06-19T22:00:50.158347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 474
32.6%
2 391
26.9%
1 303
20.8%
6 67
 
4.6%
5 51
 
3.5%
7 46
 
3.2%
4 38
 
2.6%
9 33
 
2.3%
3 29
 
2.0%
8 24
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 474
32.6%
2 391
26.9%
1 303
20.8%
6 67
 
4.6%
5 51
 
3.5%
7 46
 
3.2%
4 38
 
2.6%
9 33
 
2.3%
3 29
 
2.0%
8 24
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 474
32.6%
2 391
26.9%
1 303
20.8%
6 67
 
4.6%
5 51
 
3.5%
7 46
 
3.2%
4 38
 
2.6%
9 33
 
2.3%
3 29
 
2.0%
8 24
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 474
32.6%
2 391
26.9%
1 303
20.8%
6 67
 
4.6%
5 51
 
3.5%
7 46
 
3.2%
4 38
 
2.6%
9 33
 
2.3%
3 29
 
2.0%
8 24
 
1.6%

ENERGYSTARScore
Real number (ℝ)

MISSING 

Distinct100
Distinct (%)3.9%
Missing843
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean67.91867351
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:50.374909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q153
median75
Q390
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.87327089
Coefficient of variation (CV)0.3956683707
Kurtosis-0.2195668767
Mean67.91867351
Median Absolute Deviation (MAD)17
Skewness-0.8594613198
Sum172038
Variance722.1726883
MonotonicityNot monotonic
2024-06-19T22:00:50.593157image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 109
 
3.2%
98 72
 
2.1%
96 64
 
1.9%
89 58
 
1.7%
93 57
 
1.7%
92 53
 
1.6%
95 51
 
1.5%
94 49
 
1.5%
91 49
 
1.5%
99 49
 
1.5%
Other values (90) 1922
56.9%
(Missing) 843
25.0%
ValueCountFrequency (%)
1 36
1.1%
2 10
 
0.3%
3 13
 
0.4%
4 5
 
0.1%
5 10
 
0.3%
ValueCountFrequency (%)
100 109
3.2%
99 49
1.5%
98 72
2.1%
97 48
1.4%
96 64
1.9%

SiteEUI(kBtu/sf)
Real number (ℝ)

Distinct1085
Distinct (%)32.2%
Missing7
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean54.7321164
Minimum0
Maximum834.4000244
Zeros16
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:50.799346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.97999992
Q127.89999962
median38.59999847
Q360.40000153
95-th percentile146.9
Maximum834.4000244
Range834.4000244
Interquartile range (IQR)32.50000191

Descriptive statistics

Standard deviation56.27312409
Coefficient of variation (CV)1.028155456
Kurtosis39.99456818
Mean54.7321164
Median Absolute Deviation (MAD)13.5
Skewness4.981885737
Sum184392.5001
Variance3166.664495
MonotonicityNot monotonic
2024-06-19T22:00:51.046129image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.70000076 17
 
0.5%
28.79999924 17
 
0.5%
0 16
 
0.5%
24.20000076 16
 
0.5%
32 15
 
0.4%
26.39999962 14
 
0.4%
31.70000076 14
 
0.4%
28.89999962 14
 
0.4%
29.60000038 13
 
0.4%
22.79999924 13
 
0.4%
Other values (1075) 3220
95.4%
ValueCountFrequency (%)
0 16
0.5%
0.400000006 1
 
< 0.1%
0.699999988 1
 
< 0.1%
1 1
 
< 0.1%
1.399999976 1
 
< 0.1%
ValueCountFrequency (%)
834.4000244 1
< 0.1%
707.2999878 1
< 0.1%
696.7000122 1
< 0.1%
694.7000122 1
< 0.1%
639.7000122 1
< 0.1%

SiteEUIWN(kBtu/sf)
Real number (ℝ)

Distinct1105
Distinct (%)32.8%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean57.03379823
Minimum0
Maximum834.4000244
Zeros29
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:51.248204image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.39999962
Q129.39999962
median40.90000153
Q364.27500152
95-th percentile149.155001
Maximum834.4000244
Range834.4000244
Interquartile range (IQR)34.8750019

Descriptive statistics

Standard deviation57.16333021
Coefficient of variation (CV)1.002271144
Kurtosis37.63950264
Mean57.03379823
Median Absolute Deviation (MAD)14.30000115
Skewness4.827517733
Sum192203.9
Variance3267.646321
MonotonicityNot monotonic
2024-06-19T22:00:51.449358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.9%
29.5 17
 
0.5%
30.79999924 15
 
0.4%
27.89999962 14
 
0.4%
30.20000076 14
 
0.4%
31.60000038 14
 
0.4%
29 14
 
0.4%
32.20000076 14
 
0.4%
33.59999847 13
 
0.4%
31.39999962 13
 
0.4%
Other values (1095) 3213
95.2%
ValueCountFrequency (%)
0 29
0.9%
0.400000006 1
 
< 0.1%
0.699999988 1
 
< 0.1%
1 1
 
< 0.1%
1.5 1
 
< 0.1%
ValueCountFrequency (%)
834.4000244 1
< 0.1%
707.2999878 1
< 0.1%
694.7000122 1
< 0.1%
693.0999756 1
< 0.1%
639.7999878 1
< 0.1%

SourceEUI(kBtu/sf)
Real number (ℝ)

Distinct1648
Distinct (%)48.9%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean134.2328482
Minimum0
Maximum2620
Zeros24
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:51.639112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.85999947
Q174.69999695
median96.19999695
Q3143.8999939
95-th percentile351.6700104
Maximum2620
Range2620
Interquartile range (IQR)69.19999695

Descriptive statistics

Standard deviation139.2875538
Coefficient of variation (CV)1.037656249
Kurtosis77.66477838
Mean134.2328482
Median Absolute Deviation (MAD)27.79999542
Skewness6.595043734
Sum451962
Variance19401.02264
MonotonicityNot monotonic
2024-06-19T22:00:51.845598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
0.7%
83.69999695 9
 
0.3%
68.09999847 9
 
0.3%
90.5 8
 
0.2%
73.09999847 8
 
0.2%
78.59999847 8
 
0.2%
87.69999695 8
 
0.2%
94.09999847 8
 
0.2%
95 8
 
0.2%
69.69999695 8
 
0.2%
Other values (1638) 3269
96.8%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
0 24
0.7%
1.100000024 1
 
< 0.1%
2 1
 
< 0.1%
2.099999905 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
2620 1
< 0.1%
2217.800049 1
< 0.1%
2181.300049 1
< 0.1%
2007.900024 1
< 0.1%
1527.300049 1
< 0.1%

SourceEUIWN(kBtu/sf)
Real number (ℝ)

ZEROS 

Distinct1694
Distinct (%)50.3%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean137.7839323
Minimum-2.099999905
Maximum2620
Zeros36
Zeros (%)1.1%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2024-06-19T22:00:52.341369image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2.099999905
5-th percentile37.70000076
Q178.40000153
median101.0999985
Q3148.3499985
95-th percentile353.8600037
Maximum2620
Range2622.1
Interquartile range (IQR)69.94999697

Descriptive statistics

Standard deviation139.1098067
Coefficient of variation (CV)1.009622852
Kurtosis77.44186155
Mean137.7839323
Median Absolute Deviation (MAD)28.50000003
Skewness6.569688358
Sum463918.5
Variance19351.53831
MonotonicityNot monotonic
2024-06-19T22:00:52.570339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
1.1%
73.59999847 9
 
0.3%
87.30000305 9
 
0.3%
93.59999847 8
 
0.2%
75.5 8
 
0.2%
102.4000015 8
 
0.2%
98.90000153 8
 
0.2%
83.5 8
 
0.2%
84.90000153 8
 
0.2%
104.5999985 8
 
0.2%
Other values (1684) 3257
96.5%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
-2.099999905 1
 
< 0.1%
0 36
1.1%
1.100000024 1
 
< 0.1%
2.200000048 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
2620 1
< 0.1%
2217.800049 1
< 0.1%
2181.300049 1
< 0.1%
2008 1
< 0.1%
1527.300049 1
< 0.1%

SiteEnergyUse(kBtu)
Real number (ℝ)

SKEWED 

Distinct3354
Distinct (%)99.5%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5403667.295
Minimum0
Maximum873923712
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:52.808701image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile491819.9531
Q1925128.5938
median1803753.25
Q34222455.25
95-th percentile18161625
Maximum873923712
Range873923712
Interquartile range (IQR)3297326.656

Descriptive statistics

Standard deviation21610628.63
Coefficient of variation (CV)3.999252258
Kurtosis858.6184814
Mean5403667.295
Median Absolute Deviation (MAD)1074356.062
Skewness24.84197927
Sum1.821576245 × 1010
Variance4.670192697 × 1014
MonotonicityNot monotonic
2024-06-19T22:00:53.023928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
0.5%
586358.875 1
 
< 0.1%
12213423 1
 
< 0.1%
6714540 1
 
< 0.1%
4653535 1
 
< 0.1%
8163413 1
 
< 0.1%
415364.5938 1
 
< 0.1%
561473.875 1
 
< 0.1%
905750.375 1
 
< 0.1%
2253647.75 1
 
< 0.1%
Other values (3344) 3344
99.1%
(Missing) 5
 
0.1%
ValueCountFrequency (%)
0 18
0.5%
13409 1
 
< 0.1%
16808.90039 1
 
< 0.1%
24105.5 1
 
< 0.1%
44293.5 1
 
< 0.1%
ValueCountFrequency (%)
873923712 1
< 0.1%
448385312 1
< 0.1%
293090784 1
< 0.1%
291614432 1
< 0.1%
274682208 1
< 0.1%

SiteEnergyUseWN(kBtu)
Real number (ℝ)

Distinct3341
Distinct (%)99.1%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5276725.714
Minimum0
Maximum471613856
Zeros29
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:53.267044image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile503320.811
Q1970182.2344
median1904452
Q34381429.125
95-th percentile18203297.4
Maximum471613856
Range471613856
Interquartile range (IQR)3411246.891

Descriptive statistics

Standard deviation15938786.48
Coefficient of variation (CV)3.020582715
Kurtosis334.5050175
Mean5276725.714
Median Absolute Deviation (MAD)1130097.25
Skewness15.26906663
Sum1.778256566 × 1010
Variance2.540449146 × 1014
MonotonicityNot monotonic
2024-06-19T22:00:53.485033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
0.9%
2127889.25 2
 
0.1%
963968.1875 1
 
< 0.1%
150167.7969 1
 
< 0.1%
1386445.375 1
 
< 0.1%
1519845.5 1
 
< 0.1%
455648.9063 1
 
< 0.1%
13089102 1
 
< 0.1%
6739209 1
 
< 0.1%
4653535 1
 
< 0.1%
Other values (3331) 3331
98.7%
(Missing) 6
 
0.2%
ValueCountFrequency (%)
0 29
0.9%
13409 1
 
< 0.1%
17260 1
 
< 0.1%
24105.5 1
 
< 0.1%
44293.5 1
 
< 0.1%
ValueCountFrequency (%)
471613856 1
< 0.1%
296671744 1
< 0.1%
295929888 1
< 0.1%
274725984 1
< 0.1%
257764208 1
< 0.1%

SteamUse(kBtu)
Real number (ℝ)

SKEWED  ZEROS 

Distinct131
Distinct (%)3.9%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean274595.8982
Minimum0
Maximum134943456
Zeros3237
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:53.675809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum134943456
Range134943456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3912173.393
Coefficient of variation (CV)14.24702051
Kurtosis804.8642147
Mean274595.8982
Median Absolute Deviation (MAD)0
Skewness26.72088824
Sum924564389.3
Variance1.530510065 × 1013
MonotonicityNot monotonic
2024-06-19T22:00:53.912639image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3237
95.9%
12508892 1
 
< 0.1%
882630.6875 1
 
< 0.1%
3578548.25 1
 
< 0.1%
1158118.25 1
 
< 0.1%
1165335.75 1
 
< 0.1%
2796766 1
 
< 0.1%
2003882 1
 
< 0.1%
1472483.375 1
 
< 0.1%
230989.2969 1
 
< 0.1%
Other values (121) 121
 
3.6%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
0 3237
95.9%
21230.80078 1
 
< 0.1%
137900 1
 
< 0.1%
151742.5 1
 
< 0.1%
166488.4063 1
 
< 0.1%
ValueCountFrequency (%)
134943456 1
< 0.1%
122575032 1
< 0.1%
84985240 1
< 0.1%
73885472 1
< 0.1%
31030194 1
< 0.1%

Electricity(kWh)
Real number (ℝ)

SKEWED 

Distinct3352
Distinct (%)99.6%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1086638.967
Minimum-33826.80078
Maximum192577488
Zeros14
Zeros (%)0.4%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2024-06-19T22:00:54.114314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-33826.80078
5-th percentile72675.63985
Q1187422.9453
median345129.9063
Q3829317.8438
95-th percentile3944193.125
Maximum192577488
Range192611314.8
Interquartile range (IQR)641894.8984

Descriptive statistics

Standard deviation4352478.355
Coefficient of variation (CV)4.005450282
Kurtosis1157.498858
Mean1086638.967
Median Absolute Deviation (MAD)200696.3125
Skewness28.72846386
Sum3658713400
Variance1.894406783 × 1013
MonotonicityNot monotonic
2024-06-19T22:00:54.336154image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
0.4%
239011.5938 2
 
0.1%
317841.4063 2
 
0.1%
1156514.25 1
 
< 0.1%
154136.4063 1
 
< 0.1%
406343.8125 1
 
< 0.1%
165367.7031 1
 
< 0.1%
123502.2031 1
 
< 0.1%
1437784.625 1
 
< 0.1%
592280.875 1
 
< 0.1%
Other values (3342) 3342
99.0%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
-33826.80078 1
 
< 0.1%
0 14
0.4%
1 1
 
< 0.1%
1798.900024 1
 
< 0.1%
3332.5 1
 
< 0.1%
ValueCountFrequency (%)
192577488 1
< 0.1%
80460872 1
< 0.1%
49438336 1
< 0.1%
44102076 1
< 0.1%
40842564 1
< 0.1%

Electricity(kBtu)
Real number (ℝ)

SKEWED 

Distinct3351
Distinct (%)99.5%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean3707612.162
Minimum-115417
Maximum657074389
Zeros14
Zeros (%)0.4%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2024-06-19T22:00:54.535285image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-115417
5-th percentile247969.2
Q1639487
median1177583
Q32829632.5
95-th percentile13457586.8
Maximum657074389
Range657189806
Interquartile range (IQR)2190145.5

Descriptive statistics

Standard deviation14850656.14
Coefficient of variation (CV)4.005450271
Kurtosis1157.498861
Mean3707612.162
Median Absolute Deviation (MAD)684776
Skewness28.72846389
Sum1.248353015 × 1010
Variance2.205419878 × 1014
MonotonicityNot monotonic
2024-06-19T22:00:54.724374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
0.4%
815508 2
 
0.1%
804194 2
 
0.1%
1084475 2
 
0.1%
3946027 1
 
< 0.1%
166749 1
 
< 0.1%
138179 1
 
< 0.1%
1386445 1
 
< 0.1%
564235 1
 
< 0.1%
421390 1
 
< 0.1%
Other values (3341) 3341
99.0%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
-115417 1
 
< 0.1%
0 14
0.4%
3 1
 
< 0.1%
6138 1
 
< 0.1%
11370 1
 
< 0.1%
ValueCountFrequency (%)
657074389 1
< 0.1%
274532495 1
< 0.1%
168683602 1
< 0.1%
150476283 1
< 0.1%
139354828 1
< 0.1%

NaturalGas(therms)
Real number (ℝ)

SKEWED  ZEROS 

Distinct2109
Distinct (%)62.6%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean13685.04538
Minimum0
Maximum2979090
Zeros1258
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:54.911109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3237.537598
Q311890.33496
95-th percentile49023.72422
Maximum2979090
Range2979090
Interquartile range (IQR)11890.33496

Descriptive statistics

Standard deviation67097.8083
Coefficient of variation (CV)4.903002252
Kurtosis1201.032447
Mean13685.04538
Median Absolute Deviation (MAD)3237.537598
Skewness30.03889031
Sum46077547.78
Variance4502115878
MonotonicityNot monotonic
2024-06-19T22:00:55.129546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1258
37.3%
2268.460205 2
 
0.1%
12764.5293 1
 
< 0.1%
2486.159912 1
 
< 0.1%
119.8899918 1
 
< 0.1%
7672.350098 1
 
< 0.1%
73077.01563 1
 
< 0.1%
46936.77344 1
 
< 0.1%
52450.41797 1
 
< 0.1%
4248.649902 1
 
< 0.1%
Other values (2099) 2099
62.2%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
0 1258
37.3%
0.329999954 1
 
< 0.1%
1.530000091 1
 
< 0.1%
2.199999809 1
 
< 0.1%
3.320000172 1
 
< 0.1%
ValueCountFrequency (%)
2979090 1
< 0.1%
1381912.375 1
< 0.1%
846680.9375 1
< 0.1%
679905.375 1
< 0.1%
667464.25 1
< 0.1%

NaturalGas(kBtu)
Real number (ℝ)

SKEWED  ZEROS 

Distinct2109
Distinct (%)62.6%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1368504.541
Minimum0
Maximum297909000
Zeros1258
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:55.383752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median323754
Q31189033.5
95-th percentile4902372.3
Maximum297909000
Range297909000
Interquartile range (IQR)1189033.5

Descriptive statistics

Standard deviation6709780.835
Coefficient of variation (CV)4.903002242
Kurtosis1201.032444
Mean1368504.541
Median Absolute Deviation (MAD)323754
Skewness30.03889028
Sum4607754791
Variance4.502115885 × 1013
MonotonicityNot monotonic
2024-06-19T22:00:55.575253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1258
37.3%
226846 2
 
0.1%
1276453 1
 
< 0.1%
248616 1
 
< 0.1%
11989 1
 
< 0.1%
767235 1
 
< 0.1%
7307702 1
 
< 0.1%
4693677 1
 
< 0.1%
5245042 1
 
< 0.1%
424865 1
 
< 0.1%
Other values (2099) 2099
62.2%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
0 1258
37.3%
33 1
 
< 0.1%
153 1
 
< 0.1%
220 1
 
< 0.1%
332 1
 
< 0.1%
ValueCountFrequency (%)
297909000 1
< 0.1%
138191238 1
< 0.1%
84668094 1
< 0.1%
67990538 1
< 0.1%
66746425 1
< 0.1%

DefaultData
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3263 
True
 
113
ValueCountFrequency (%)
False 3263
96.7%
True 113
 
3.3%
2024-06-19T22:00:55.724592image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Comments
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3376
Missing (%)100.0%
Memory size26.5 KiB
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2024-06-19T22:00:55.841447image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length28
Median length9
Mean length9.693127962
Min length9

Characters and Unicode

Total characters32724
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompliant
2nd rowCompliant
3rd rowCompliant
4th rowCompliant
5th rowCompliant
ValueCountFrequency (%)
compliant 3211
83.6%
data 128
 
3.3%
error 113
 
2.9%
113
 
2.9%
correct 113
 
2.9%
default 113
 
2.9%
non-compliant 37
 
1.0%
missing 15
 
0.4%
2024-06-19T22:00:56.161601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3617
11.1%
t 3602
11.0%
o 3511
10.7%
C 3361
10.3%
l 3361
10.3%
n 3300
10.1%
i 3278
10.0%
m 3248
9.9%
p 3248
9.9%
r 565
 
1.7%
Other values (12) 1633
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3617
11.1%
t 3602
11.0%
o 3511
10.7%
C 3361
10.3%
l 3361
10.3%
n 3300
10.1%
i 3278
10.0%
m 3248
9.9%
p 3248
9.9%
r 565
 
1.7%
Other values (12) 1633
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3617
11.1%
t 3602
11.0%
o 3511
10.7%
C 3361
10.3%
l 3361
10.3%
n 3300
10.1%
i 3278
10.0%
m 3248
9.9%
p 3248
9.9%
r 565
 
1.7%
Other values (12) 1633
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3617
11.1%
t 3602
11.0%
o 3511
10.7%
C 3361
10.3%
l 3361
10.3%
n 3300
10.1%
i 3278
10.0%
m 3248
9.9%
p 3248
9.9%
r 565
 
1.7%
Other values (12) 1633
5.0%

Outlier
Text

MISSING 

Distinct2
Distinct (%)6.2%
Missing3344
Missing (%)99.1%
Memory size26.5 KiB
2024-06-19T22:00:56.296973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.28125
Min length11

Characters and Unicode

Total characters361
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh outlier
2nd rowLow outlier
3rd rowLow outlier
4th rowHigh outlier
5th rowLow outlier
ValueCountFrequency (%)
outlier 32
50.0%
low 23
35.9%
high 9
 
14.1%
2024-06-19T22:00:56.619472image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 55
15.2%
i 41
11.4%
32
8.9%
u 32
8.9%
t 32
8.9%
l 32
8.9%
e 32
8.9%
r 32
8.9%
L 23
6.4%
w 23
6.4%
Other values (3) 27
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 361
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 55
15.2%
i 41
11.4%
32
8.9%
u 32
8.9%
t 32
8.9%
l 32
8.9%
e 32
8.9%
r 32
8.9%
L 23
6.4%
w 23
6.4%
Other values (3) 27
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 361
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 55
15.2%
i 41
11.4%
32
8.9%
u 32
8.9%
t 32
8.9%
l 32
8.9%
e 32
8.9%
r 32
8.9%
L 23
6.4%
w 23
6.4%
Other values (3) 27
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 361
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 55
15.2%
i 41
11.4%
32
8.9%
u 32
8.9%
t 32
8.9%
l 32
8.9%
e 32
8.9%
r 32
8.9%
L 23
6.4%
w 23
6.4%
Other values (3) 27
7.5%

TotalGHGEmissions
Real number (ℝ)

Distinct2818
Distinct (%)83.7%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean119.7239709
Minimum-0.8
Maximum16870.98
Zeros9
Zeros (%)0.3%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2024-06-19T22:00:56.827101image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile3.78
Q19.495
median33.92
Q393.94
95-th percentile392.797
Maximum16870.98
Range16871.78
Interquartile range (IQR)84.445

Descriptive statistics

Standard deviation538.8322265
Coefficient of variation (CV)4.500621074
Kurtosis474.8922233
Mean119.7239709
Median Absolute Deviation (MAD)27.94
Skewness19.48187492
Sum403110.61
Variance290340.1683
MonotonicityNot monotonic
2024-06-19T22:00:57.039553image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
0.3%
3.95 7
 
0.2%
4.2 6
 
0.2%
5.46 6
 
0.2%
4.74 5
 
0.1%
5.07 5
 
0.1%
6.18 5
 
0.1%
3.63 5
 
0.1%
4.8 5
 
0.1%
4.02 5
 
0.1%
Other values (2808) 3309
98.0%
(Missing) 9
 
0.3%
ValueCountFrequency (%)
-0.8 1
 
< 0.1%
0 9
0.3%
0.09 1
 
< 0.1%
0.12 1
 
< 0.1%
0.17 1
 
< 0.1%
ValueCountFrequency (%)
16870.98 1
< 0.1%
12307.16 1
< 0.1%
11140.56 1
< 0.1%
10734.57 1
< 0.1%
8145.52 1
< 0.1%

GHGEmissionsIntensity
Real number (ℝ)

Distinct511
Distinct (%)15.2%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.175916246
Minimum-0.02
Maximum34.09
Zeros12
Zeros (%)0.4%
Negative1
Negative (%)< 0.1%
Memory size26.5 KiB
2024-06-19T22:00:57.264733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.02
5-th percentile0.13
Q10.21
median0.61
Q31.37
95-th percentile3.961
Maximum34.09
Range34.11
Interquartile range (IQR)1.16

Descriptive statistics

Standard deviation1.821451788
Coefficient of variation (CV)1.548963878
Kurtosis57.37215629
Mean1.175916246
Median Absolute Deviation (MAD)0.44
Skewness5.593144823
Sum3959.31
Variance3.317686616
MonotonicityNot monotonic
2024-06-19T22:00:57.528588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17 99
 
2.9%
0.15 99
 
2.9%
0.16 96
 
2.8%
0.18 86
 
2.5%
0.19 78
 
2.3%
0.2 70
 
2.1%
0.13 66
 
2.0%
0.14 62
 
1.8%
0.21 60
 
1.8%
0.22 54
 
1.6%
Other values (501) 2597
76.9%
ValueCountFrequency (%)
-0.02 1
 
< 0.1%
0 12
0.4%
0.01 4
 
0.1%
0.02 4
 
0.1%
0.03 7
0.2%
ValueCountFrequency (%)
34.09 1
< 0.1%
25.71 1
< 0.1%
16.99 1
< 0.1%
16.93 1
< 0.1%
16.91 1
< 0.1%